{"id":"https://openalex.org/W2904936253","doi":"https://doi.org/10.1109/itsc.2018.8569958","title":"Efficient Dense-Dilation Network for Pavement Cracks Detection with Large Input Image Size","display_name":"Efficient Dense-Dilation Network for Pavement Cracks Detection with Large Input Image Size","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2904936253","doi":"https://doi.org/10.1109/itsc.2018.8569958","mag":"2904936253"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2018.8569958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102736913","display_name":"Kaige Zhang","orcid":"https://orcid.org/0000-0003-0935-2734"},"institutions":[{"id":"https://openalex.org/I121980950","display_name":"Utah State University","ror":"https://ror.org/00h6set76","country_code":"US","type":"education","lineage":["https://openalex.org/I121980950"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaige Zhang","raw_affiliation_strings":["Dept. of Computer Science, Utah State University, Logan, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, Utah State University, Logan, USA","institution_ids":["https://openalex.org/I121980950"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101890714","display_name":"Heng-Da Cheng","orcid":"https://orcid.org/0000-0003-3569-7621"},"institutions":[{"id":"https://openalex.org/I121980950","display_name":"Utah State University","ror":"https://ror.org/00h6set76","country_code":"US","type":"education","lineage":["https://openalex.org/I121980950"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heng-Da Cheng","raw_affiliation_strings":["Dept. of Computer Science, Utah State University, Logan, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, Utah State University, Logan, USA","institution_ids":["https://openalex.org/I121980950"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032806691","display_name":"Shan Gai","orcid":"https://orcid.org/0000-0001-6139-1410"},"institutions":[{"id":"https://openalex.org/I927504317","display_name":"Nanchang Hangkong University","ror":"https://ror.org/0369pvp92","country_code":"CN","type":"education","lineage":["https://openalex.org/I927504317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shan Gai","raw_affiliation_strings":["Dept. of Information Engineering, Nanchang Hangkong University, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Information Engineering, Nanchang Hangkong University, Nanchang, China","institution_ids":["https://openalex.org/I927504317"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6252,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.83415433,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"884","last_page":"889"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10264","display_name":"Asphalt Pavement Performance Evaluation","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9629999995231628,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dilation","display_name":"Dilation (metric space)","score":0.877690315246582},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7418510317802429},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6260875463485718},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6096387505531311},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6087603569030762},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5291212797164917},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5263662934303284},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5040942430496216},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.47504284977912903},{"id":"https://openalex.org/keywords/mathematical-morphology","display_name":"Mathematical morphology","score":0.4583754539489746},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4562883973121643},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4347769618034363},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4139404892921448},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38758188486099243},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3156941533088684},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1016453206539154}],"concepts":[{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.877690315246582},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7418510317802429},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6260875463485718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6096387505531311},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6087603569030762},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5291212797164917},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5263662934303284},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5040942430496216},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.47504284977912903},{"id":"https://openalex.org/C185568154","wikidata":"https://www.wikidata.org/wiki/Q530242","display_name":"Mathematical morphology","level":4,"score":0.4583754539489746},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4562883973121643},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4347769618034363},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4139404892921448},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38758188486099243},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3156941533088684},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1016453206539154},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2018.8569958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1533162639","https://openalex.org/W1536680647","https://openalex.org/W1862829300","https://openalex.org/W1903029394","https://openalex.org/W2037227137","https://openalex.org/W2074925468","https://openalex.org/W2075631365","https://openalex.org/W2095705004","https://openalex.org/W2102605133","https://openalex.org/W2149933564","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2407692387","https://openalex.org/W2511065100","https://openalex.org/W2598457882","https://openalex.org/W2745396798","https://openalex.org/W2782408838","https://openalex.org/W2950094539","https://openalex.org/W2962887362","https://openalex.org/W2963542991","https://openalex.org/W2963840672","https://openalex.org/W4299518610","https://openalex.org/W6629368666","https://openalex.org/W6674330103","https://openalex.org/W6682132143","https://openalex.org/W6696085341"],"related_works":["https://openalex.org/W2429125578","https://openalex.org/W2160935107","https://openalex.org/W2109932938","https://openalex.org/W2144541549","https://openalex.org/W2604155019","https://openalex.org/W4298005417","https://openalex.org/W2019913337","https://openalex.org/W2350127111","https://openalex.org/W4252710998","https://openalex.org/W2892082603"],"abstract_inverted_index":{"Window-sliding/region-proposal":[0],"based":[1],"methods":[2,18],"have":[3],"been":[4],"the":[5,23,56,80,83,104,107,115,126,133,140,146,151,157,184],"popular":[6],"approaches":[7],"for":[8,35,79],"object":[9],"detection":[10,120],"with":[11,64,181],"deep":[12],"convolutional":[13,52,109],"neural":[14],"networks.":[15],"However,":[16],"these":[17],"are":[19],"very":[20],"inefficient":[21],"when":[22],"input":[24,127],"image":[25,66,95],"size":[26],"is":[27,62,111,123,164],"large,":[28],"such":[29],"as":[30,97],"pavement":[31,168],"images":[32,169],"(2000\u00d74000-pixel)":[33,170],"used":[34],"cracking":[36],"detection.":[37],"In":[38,82,103],"this":[39,46],"paper,":[40],"we":[41],"propose":[42],"a":[43,50,88,93,119],"solution":[44],"to":[45,76,113,125,149],"problem":[47],"by":[48,172],"introducing":[49,132],"fully":[51,108],"dense-dilation":[53,135],"network":[54,61,90,117,121,148,153],"and":[55,142,155,175,190],"corresponding":[57],"training":[58],"strategy.":[59],"The":[60,161],"trained":[63],"small":[65,94],"blocks,":[67],"then":[68],"works":[69,187],"on":[70,166],"full-size":[71],"images,":[72],"which":[73,91],"only":[74],"needs":[75],"forward":[77],"once":[78],"process.":[81],"first":[84],"phase,":[85,106],"it":[86,137,176],"trains":[87],"classification":[89,116,147],"classifies":[92],"block":[96],"crack,":[98],"sealed":[99],"crack":[100,158],"or":[101],"background.":[102],"second":[105],"layer":[110],"employed":[112],"convert":[114],"into":[118],"that":[122,182],"insensitive":[124],"size.":[128],"At":[129],"last,":[130],"via":[131],"equivalent":[134],"design,":[136],"transfers":[138],"both":[139],"low-level":[141],"middle-level":[143],"knowledge":[144],"from":[145],"facilitate":[150],"end-to-end":[152],"refining":[154],"improve":[156],"localization":[159],"accuracy.":[160,191],"proposed":[162],"approach":[163],"validated":[165],"600":[167],"obtained":[171],"industry":[173],"equipment":[174],"achieves":[177],"state-of-the-art":[178],"performance":[179],"comparing":[180],"of":[183],"recently":[185],"published":[186],"in":[188],"efficiency":[189]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
